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Statistical Analysis with ArcView GIS

Statistical Analysis with ArcView GIS by Jay Lee, David Wing - Shun Wong

The ArcView GIS Avenue scripts referenced throughout Statistical Analysis with ArcView GIS are available for download below. Two packages are available:

Simply download the desired archive, extract the files to your local drive, and navigate the folders as described in the ArcView Notes found throughout the text. Each archive is approximately 4.5 MB.

The .zip file can be extracted using the freely available WinZip program, www.winzip.com
The .sit file can be extracted using the freely available StuffIt Expander from Alladin Systems, www.aladdinsys.com/expander

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ABOUT THE BOOK

Statistical analysis of geographic data has been greatly enhanced in recent years with the advent of Geographical Information Systems (GIS) software. Yet GIS users have struggles to synchronize their applications of spatial information with practical, quantitative statistics. ArcView, one of the most powerful GIS-compatible systems, has become the most popular software among geographers precisely because of its capacity for spatial-quantitative synthesis. Now geographers Jay Lee and David Wong have produced the first handbook for applied ArcView use, bringing the theoretical underpinnings of classical statistics into the earth science environment.

Employing points, lines, and polygons to model real-world geographic forms, this easy-to-use resource provides geographers with a valuable bridge between theory and the software necessary to apply it. It contains sections on point distribution, point pattern analysis, linear features, network analysis, and spatial autocorrelation analysis. Statistical Analysis with ArcView GIS also features:

  • Examples that show steps of statistical calculations—as well as ways to interpret the results
  • More than 100 illustrations, including statistical charts, maps, and ArcView screen captures
  • Helpful end-of-chapter references

Suitable for professionals as well as students of geography, this book is an important tool for anyone involved in the statistical analysis of GIS data.

Jay Lee, PhD, is Associate Professor of Geography at Kent State University in Ohio and served as associate editor of the Wiley journal, Applied Geographic Studies.

David W.S. Wong, PhD, is Associate Professor of Earth Sciences at George Mason University in Fairfax, Virginia.

Table of Contents

  • Attribute Descriptors.
  • Point Descriptors.
  • Pattern Detectors.
  • Line Descriptors.
  • Pattern Descriptors.
  • Index.

The authors welcome feedback regarding the book or scripts. Jay Lee is available via email at jlee@kent.edu. David Wong is available at dwong2@gmu.edu.

For Technical Support, please email techhelp@wiley.com